{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split \n",
    "import numpy as np\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uid = pd.read_csv('../predict_unlabel/preds/unlabel_pred.csv',usecols=['id']).values.ravel()\n",
    "score_KGB = pd.read_csv('../predict_unlabel/preds/unlabel_pred.csv')['score']\n",
    "score_AR = pd.read_csv('../predict_tag_new/unlabel_tag_score.csv')['score']\n",
    "score_com = alpha*score_AR + (1-alpha)*score_KGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_com_df =pd.DataFrame()\n",
    "score_com_df['id'] = uid\n",
    "score_com_df['score'] = score_com\n",
    "score_com_df.to_csv('./score_com_df.csv',index=False)"
   ]
  }
 ],
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